Enhanced accuracy for speech recognition grammars

ABSTRACT

Disclosed herein are methods and systems for recognizing speech. A method embodiment comprises comparing received speech with a precompiled grammar based on a database and if the received speech matches data in the precompiled grammar then returning a result based on the matched data. If the received speech does not match data in the precompiled grammar, then dynamically compiling a new grammar based only on new data added to the database after the compiling of the precompiled grammar. The database may comprise a directory of names.

PRIORITY INFORMATION

The present application is a continuation of U.S. patent applicationSer. No. 11/470,685, filed Sep. 7, 2006, the content of which isincluded herewith in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a system and method of providinginteractive response system and more specifically to a system and methodof providing an improved method and system for performing speechrecognition.

2. Introduction

Many entities utilize interactive voice systems to communicate withpeople. For example, there are many natural language dialog systemswhere a caller can call the system and hear a prompt such as “how may Ihelp you?”. The user can then talk to the computer which utilizesprogramming modules such as an automatic speech recognition module(ASR), a spoken language understanding module (SLU), a dialog managementmodule (DM) and a text-to-speech module (TTS) or any other type ofspeech generation module to communicate. As these names indicate, eachmodule will carry out respective functions such as transforming voicesignals into text, determining the meaning of that text, generating adialog response and converting text into audible speech or playing backrecorded audio so that the user can hear the response. These basiccomponents are known to those of skill in the art.

Building a dialog system is costly and takes highly trained technicians.Systems may be built utilizing one or more features of a spoken dialogsystem. For example, a large company may maintain a directory systemaccessible via automatic speech recognition. In this case the names inthe database are precompiled into what is known by those of skill in theart as a recognition grammar. This increases the recognition success forthe application by limiting the number of tokens or names that can berecognized by the grammar to those in the actual database, rather thanusing an extremely large set of all possible names.

But this precompiling process can be the source of difficulties. Forexample, the recognition grammar must be recompiled periodically due tochanges in the names in the database. Names may be added or removed.Constantly updating and recompiling a large recognition grammar may becostly and time consuming.

Therefore, what is needed in the art is an improved way of providingspeech recognition where the recognition grammar is associated with anunderlying database that is constantly changing.

SUMMARY OF THE INVENTION

Additional features and advantages of the invention will be set forth inthe description which follows, and in part will be obvious from thedescription, or may be learned by practice of the invention. Thefeatures and advantages of the invention may be realized and obtained bymeans of the instruments and combinations particularly pointed out inthe appended claims. These and other features of the present inventionwill become more fully apparent from the following description andappended claims, or may be learned by the practice of the invention asset forth herein.

The present invention provides for systems, methods andcomputer-readable media for recognizing speech. The method embodimentcomprises comparing received speech with a precompiled grammar based ona database, if the received speech matches data in the precompileddatabase, then returning a result based on the matched data. If thereceived speech does not match data in the precompiled grammar, thendynamically compiling a new grammar based only on new data added to thedatabase after the compiling of the precompiled grammar. The new datamay be obtained from a source such as a table, a list or a database ofupdates to the database associated with the compiling of the precompileddata. In a preferred embodiment, the database is associated with adirectory of names.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the invention can be obtained, a moreparticular description of the invention briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only typical embodiments of the invention and are not thereforeto be considered to be limiting of its scope, the invention will bedescribed and explained with additional specificity and detail throughthe use of the accompanying drawings in which:

FIG. 1 illustrates a block diagram of a spoken dialog system;

FIG. 2 illustrates basic components in a system embodiment; and

FIG. 3 illustrates a method embodiment.

DETAILED DESCRIPTION OF THE INVENTION

Various embodiments of the invention are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the invention.In particular, the invention is not limited to natural languageinteractive voice response systems but can be applied equally to systemsthat use simple speech recognition.

As mentioned above, interactive voice response dialog systems aim toidentify intents of humans and take actions accordingly, to satisfytheir requests. The present invention may apply to a spoken dialogsystem, a natural dialog system or to a simple speech recognitionsystem. The various components of a spoken dialog system are shown butnot meant to limit the structure of the invention to a more complicatedapplication than is necessary. FIG. 1 is a functional block diagram ofan exemplary natural language spoken dialog system 100. Natural languagespoken dialog system 100 may include an automatic speech recognition(ASR) module 102, a spoken language understanding (SLU) module 104, adialog management (DM) module 106, a spoken language generation (SLG)module 108, and a text-to-speech (TTS) module 110. The present inventionfocuses on innovations related to how to manage the call-flow and how tomake changes to the call-flow.

ASR module 102 may analyze speech input and may provide a transcriptionof the speech input as output SLU module 104 may receive the transcribedinput and may use a natural language understanding model to analyze thegroup of words that are included in the transcribed input to derive ameaning from the input. The role of DM module 106 is to interact in anatural way and help the user to achieve the task that the system isdesigned to support. DM module 106 may receive the meaning of the speechinput from SLU module 104 and may determine an action, such as, forexample, providing a response, based on the input. SLG module 108 maygenerate a transcription of one or more words in response to the actionprovided by DM 106. TTS module 110 may receive the transcription asinput and may provide generated audible speech as output based on thetranscribed speech. Any other method of producing speech may be used aswell, such as presenting pre-recorded speech, concatenating speechsegments or any other know method.

Thus, the modules of system 100 may recognize speech input, such asspeech utterances, may transcribe the speech input, may identify (orunderstand) the meaning of the transcribed speech, may determine anappropriate response to the speech input, may generate text of theappropriate response and from that text, may generate audible “speech”from system 100, which the user then hears. In this manner, the user cancarry on a natural language dialog with system 100. Those of ordinaryskill in the art will understand the programming languages and means forgenerating and training ASR module 102 or any of the other modules inthe spoken dialog system. Further, the modules of system 100 may operateindependent of a full dialog system. For example, a computing devicesuch as a smartphone (or any processing device having a phonecapability) may have an ASR module wherein a user may say “call mom” andthe smartphone may act on the instruction without a “spoken dialog.”

FIG. 2 illustrates an exemplary processing system 200 in which one ormore of the modules of system 100 may be implemented. Thus, system 100may include at least one processing system, such as, for example,exemplary processing system 200. System 200 may include a bus 210, aprocessor 220, a memory 230, a read only memory (ROM) 240, a storagedevice 250, an input device 260, an output device 270, and acommunication interface 280. Bus 210 may permit communication among thecomponents of system 200. Where the inventions disclosed herein relateto the TTS voice, the output device may include a speaker that generatesthe audible sound representing the computer-synthesized speech.

Processor 220 may include at least one conventional processor ormicroprocessor that interprets and executes instructions. Memory 230 maybe a random access memory (RAM) or another type of dynamic storagedevice that stores information and instructions for execution byprocessor 220. Memory 230 may also store temporary variables or otherintermediate information used during execution of instructions byprocessor 220. ROM 240 may include a conventional ROM device or anothertype of static storage device that stores static information andinstructions for processor 220. Storage device 250 may include any typeof media, such as, for example, magnetic or optical recording media andits corresponding drive.

Input device 260 may include one or more conventional mechanisms thatpermit a user to input information to system 200, such as a keyboard, amouse, a pen, motion input, a voice recognition device, a DTMF decoder,etc. Output device 270 may include one or more conventional mechanismsthat output information to the user, including a display, a printer, oneor more speakers, or a medium, such as a memory, or a magnetic oroptical disk and a corresponding disk drive. Communication interface 280may include any transceiver-like mechanism that enables system 200 tocommunicate via a network. For example, communication interface 280 mayinclude a modem, or an Ethernet interface for communicating via a localarea network (LAN). Alternatively, communication interface 280 mayinclude other mechanisms for communicating with other devices and/orsystems via wired, wireless or optical connections. In someimplementations of natural spoken dialog system 100, communicationinterface 280 may not be included in processing system 200 when naturalspoken dialog system 100 is implemented completely within a singleprocessing system 200.

System 200 may perform such functions in response to processor 220executing sequences of instructions contained in a computer-readablemedium, such as, for example, memory 230, a magnetic disk, or an opticaldisk. Such instructions may be read into memory 230 from anothercomputer-readable medium, such as storage device 250, or from a separatedevice via communication interface 280.

An embodiment of the invention relates to a method of recognizingspeech. The method comprises comparing received speech with aprecompiled grammar based on a database (302), and, if the receivedspeech matches data in the precompiled grammar, then returning a resultbased on the matched data (304). If the received speech does not matchdata in the precompiled grammar then the method comprises dynamicallycompiling a new grammar based only on new data added to the databaseafter the compiling of the precompiled grammar (306). The new data maybe obtained from at least one of a table, a list or a database ofupdates to the database that was used to build the precompiled grammar.In a preferable embodiment the database comprises a directory of names.The step of determining whether the received speech data matches data inthe precompiled grammar may further comprise determining whether thereceived speech data matches data in the precompiled grammar accordingto a given threshold.

The concept disclosed herein solves the problem of a large expensetypically associated with regular updates to databases such as employeename grammars used in automatic speech recognition applications. In thepast, one approach has been to include essentially millions of nameswithin the grammar. This solution has broad coverage but limitedaccuracy, because many names that are not employees can be recognized. Amore common approach is to limit the names in the grammar to currentemployees. This method has a higher accuracy with automatic speechrecognition but is costly to maintain because additions to this type ofrecognizer grammar require recompiling the entire grammar. Accordingly,using the principles disclosed herein grammar builds can be done muchless frequently and with fewer names and recent additions can still berecognized by the application. One aspect disclosed herein is a solutionto the problem by partitioning the name recognition problem into twoparts. In the first part, as it is typically done, the caller speaks aname and the acoustic representation of the spoken name is compared to aprecompiled grammar of employee names. If the database relates to thesome other data such as places or products or any other kind of data thereceived speech input is compared to a precompiled grammar associatedwith the data in the database. If a match is found above a giventhreshold the application proceeds normally. However, according to theprinciples disclosed herein, if the application returns a no match forthe data such as the spoken name, or no match above a given threshold,the application will then compare the acoustic waveform to a dynamicallycreated grammar comprised of just updated data such as names since thelast time the precompiled grammar was compiled. This secondary grammaris built on the fly and dynamically by reference to a table list ordatabase of data updates.

The work and processes involved in updating databases for an ASR grammarrequired by described applications are both time consuming and costly.Aside from the expense, this means that updates are done lessfrequently. Using the principles disclosed herein, updates are cheaper,faster and can be done more frequently. The small grammars contain onlyadditions since deletions and changes are transparent from theapplication standpoint, and can be modified quite often withoutdevelopmental processes.

Embodiments within the scope of the present invention may also includecomputer-readable media for carrying or having computer-executableinstructions or data structures stored thereon. Such computer-readablemedia can be any available media that can be accessed by a generalpurpose or special purpose computer. By way of example, and notlimitation, such computer-readable media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to carryor store desired program code means in the form of computer-executableinstructions or data structures. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or combination thereof) to a computer, the computerproperly views the connection as a computer-readable medium. Thus, anysuch connection is properly termed a computer-readable medium.Combinations of the above should also be included within the scope ofthe computer-readable media.

Computer-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Computer-executable instructions also includeprogram modules that are executed by computers in stand-alone or networkenvironments. Generally, program modules include routines, programs,objects, components, and data structures, etc. that perform particulartasks or implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

Those of skill in the art will appreciate that other embodiments of theinvention may be practiced in network computing environments with manytypes of computer system configurations, including personal computers,hand-held devices, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, and the like. Embodiments may also be practiced indistributed computing environments where tasks are performed by localand remote processing devices that are linked (either by hardwiredlinks, wireless links, or by a combination thereof) through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote memory storage devices.

Although the above description may contain specific details, they shouldnot be construed as limiting the claims in any way. Other configurationsof the described embodiments of the invention are part of the scope ofthis invention. For example, the principles here could apply to anyspoken dialog system in any context. Furthermore, the precompileddatabase may be focused on any type of data. Names are mentioned aboveas a preferred embodiment because of the dynamic nature of directorydatabases, but the database may comprise addresses, classes, anygeographic name or description, physical attributes, historical data,etc. Thus while there may be preferred types of databases, such as namedirectories, the principles of the invention may apply to any database.Accordingly, the appended claims and their legal equivalents should onlydefine the invention, rather than any specific examples given.

1. A method of recognizing speech, the method comprising: comparingreceived speech with a precompiled grammar based on a database; if thereceived speech matches data in the precompiled grammar, then returningthe result based on the matched data; and if the received speech doesnot match data in the precompiled grammar, then dynamically compiling anew grammar based only on new data added to the database after thecompiling of the precompiled grammar.